class_docstr: A Linear Gaussian Bayesian Network is a Bayesian Network, all
  of whose variables are continuous, and where all of the CPDs
  are linear Gaussians.
  
  An important result is that the linear Gaussian Bayesian Networks
  are an alternative representation for the class of multivariate
  Gaussian distributions.
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docstr: Returns the cpd of the node. If node is not specified returns all the CPDs
  that have been added till now to the graph
  
  Parameter
  ---------
  node: any hashable python object (optional)
      The node whose CPD we want. If node not specified returns all the
      CPDs added to the model.
  
  Returns
  -------
  A list of linear Gaussian CPDs.
  
  Examples
  --------
  >>> from pgmpy.models import LinearGaussianBayesianNetwork
  >>> from pgmpy.factors.continuous import LinearGaussianCPD
  >>> model = LinearGaussianBayesianNetwork([('x1', 'x2'), ('x2', 'x3')])
  >>> cpd1 = LinearGaussianCPD('x1', [1], 4)
  >>> cpd2 = LinearGaussianCPD('x2', [-5, 0.5], 4, ['x1'])
  >>> cpd3 = LinearGaussianCPD('x3', [4, -1], 3, ['x2'])
  >>> model.add_cpds(cpd1, cpd2, cpd3)
  >>> model.get_cpds()
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code: def get_cpds(self, node=None):
      """
      Returns the cpd of the node. If node is not specified returns all the CPDs
      that have been added till now to the graph
  
      Parameter
      ---------
      node: any hashable python object (optional)
          The node whose CPD we want. If node not specified returns all the
          CPDs added to the model.
  
      Returns
      -------
      A list of linear Gaussian CPDs.
  
      Examples
      --------
      >>> from pgmpy.models import LinearGaussianBayesianNetwork
      >>> from pgmpy.factors.continuous import LinearGaussianCPD
      >>> model = LinearGaussianBayesianNetwork([('x1', 'x2'), ('x2', 'x3')])
      >>> cpd1 = LinearGaussianCPD('x1', [1], 4)
      >>> cpd2 = LinearGaussianCPD('x2', [-5, 0.5], 4, ['x1'])
      >>> cpd3 = LinearGaussianCPD('x3', [4, -1], 3, ['x2'])
      >>> model.add_cpds(cpd1, cpd2, cpd3)
      >>> model.get_cpds()
      """
      return super(LinearGaussianBayesianNetwork, self).get_cpds(node)
  
file_path: pgmpy/pgmpy/models/LinearGaussianBayesianNetwork.py
imports: import networkx
  import numpy
  from pgmpy.factors.continuous import LinearGaussianCPD
  from pgmpy.factors.distributions import GaussianDistribution
  from pgmpy.global_vars import logger
  from pgmpy.models import BayesianNetwork
label_desc: Use image node_id 2 for calling the LinearGaussianBayesianNetwork obj's underlying member method code with example usage: obj.get_cpds(node) and returns: super